Purpose: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) shows high sensitivity in detecting breast cancer. However, its performance could be affected by patient motion during the imaging. To overcome this problem, it is necessary to correct patient motion by deformable registration, before using the DCE-MRI to detect breast cancer. However, deformable registration of DCE-MR images is challenging due to the dramatic contrast change over time (especially between the precontrast and postcontrast images). Most existing methods typically register each postcontrast image onto the precontrast image independently, without considering the dynamic contrast change after agent uptake. This could lead to the inconsistency among the aligned postcontrast images in the precontrast image space, which will eventually result in worse performance in cancer detection. In this paper, the authors present a novel hierarchical registration framework to address this problem. Methods: First, the authors propose a hierarchical registration framework to deploy the groupwise registration for simultaneous registration of all postcontrast images onto their group-mean image and further aligning the group-mean image of postcontrast images onto the precontrast image space for final alignment of all precontrast and postcontrast images. In this way, the postcontrast images (with similar intensity patterns) can be jointly aligned onto the precontrast image for increasing their overall consistency after registration. Second, in order to improve the registration between the precontrast image and the group-mean image of the postcontrast images, the authors propose using the contrast-invariant attribute vectors to guide the robust feature matching during the registration. Results: Our proposed hierarchical registration framework has been comprehensively evaluated and compared with affine registration and widely used deformable registration methods in both pairwise and groupwise registration formulation. The experimental results on both real and simulated images show that our method can obtain not only more accurate but also more consistent registration results than any of all other registration algorithms.Conclusions: The authors have proposed a novel groupwise registration method to achieve accurate and consistent alignment for breast DCE-MR images. In the future, the authors will further evaluate our proposed method with more clinical datasets.
- breast tumor image
- dynamic contrast-enhanced (DCE) MRI
- feature-based deformable registration
- groupwise registration
- local steering kernel
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging